Effects of UK hostile environment policies on maternity care for refugees, asylum seekers, and undocumented migrants in Camden: Examining the experiences of healthcare professionals and community organisations
Bibliographic record
Abstract
• Camden's growing refugee, asylum seeker, and undocumented migrant population faces increasing barriers to maternity care access. • Immigration policies undermine NHS care principles and create distrust among migrants. • Healthcare professionals and community organisations exceed remits to ensure migrants access adequate quality maternity care. • Advocacy is needed for healthcare navigator roles and comprehensive training for care providers. The London borough of Camden has long been home for many refugees, asylum seekers, and undocumented migrants (RASUs). Over time, it has witnessed an increase in the population of these migrant groups, accompanied by notable changes in the obstacles they encounter when seeking health services, particularly maternity care. We explore how the ‘hostile environment’ policies affect access to and delivery of quality maternity services for RASUs. This study was conducted over eight months (November 2021–July 2022) both remotely and face-to-face, in various locations in Camden and in the Maternity Department at University College London Hospital, UK. Healthcare professionals (HCPs) and community organisations (COs) were identified as two major stakeholders involved in the care provision for RASUs. 33 semi-structured interviews were conducted (with 22 HCPs and 11 COs) to understand their experiences of delivering care to this population. There was consensus among HCPs and COs that the current immigration policies undermined their duty of care, personal morals, and the principles of the NHS. These policies have created a restrictive environment, making it increasingly difficult for migrants to navigate the healthcare system and creating an atmosphere of distrust, propagating fears of being charged. This has led to HCPs and COs going beyond their remits to ensure that RASUs are accessing and engaging with maternity care, regardless of an individual's status and despite any potential repercussions for themselves. In the face of an intensifying hostile environment under the UK government, supporting RASUs cannot be solely reliant on political measures. We need to advocate for healthcare navigator roles, health justice partnerships, specialist teams, and comprehensive training for service providers. HCPs and COs should be adequately supported in their endeavours to ensure RASUs have access to standardised, high-quality maternity care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".